Analytical Modeling of Wind Farms: A New Approach for Power Prediction

Niayifar, Amin; Porté‐Agel, Fernando

doi:10.3390/en9090741

Cited by 267 publications

(292 citation statements)

References 27 publications

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“…The linear relationship of Equation (16) is similar to the one used in [38] by fitting a straight line to the data presented in [11], k * = 0.383 TI x + 0.0037 (presented as a dashed black line in Figure 10). When using these relationships, it is important to take into account the variability of the data, which indicates that it is not uncommon to find wake growths that differ by a factor of two or three for very similar conditions of longitudinal turbulence intensity.…”

Section: Resultsmentioning

confidence: 91%

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Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation

2018

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This study presents the setup, methodology and results from a measurement campaign dedicated to the characterization of full-scale wind turbine wakes under different inflow conditions. The measurements have been obtained from two pulsed scanning Doppler lidars mounted on the nacelle of a 2.5 MW wind turbine. The first lidar is upstream oriented and dedicated to the characterization of the inflow with a variety of scanning patterns, while the second one is downstream oriented and performs horizontal planar scans of the wake. The calculated velocity deficit profiles exhibit self-similarity in the far wake region and they can be fitted accurately to Gaussian functions. This allows for the study of the growth rate of the wake width and the recovery of the wind speed, as well as the extent of the near-wake region. The results show that a higher incoming turbulence intensity enhances the entrainment and flow mixing in the wake region, resulting in a shorter near-wake length, a faster growth rate of the wake width and a faster recovery of the velocity deficit. The relationships obtained are compared to analytical models for wind turbine wakes and allow to correct the parameters prescribed until now, which were obtained from wind-tunnel measurements and large-eddy simulations (LES), with new, more accurate values directly derived from full-scale experiments.

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Section: Resultsmentioning

confidence: 91%

“…In blue all the data collected during the experiment, in dashed red the linear fit to the full-scale field data presented in Equation (16). In black the data obtained from [11] and the linear fit used in [38]. Numbers 1 to 3 indicate the cases presented in Figure 9.…”

Section: Resultsmentioning

confidence: 99%

Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation

2018

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“…Atmospheric Conditions: This model also accounts for physical atmospheric quantities such as shear, veer, and changes in turbulence intensity (Abkar and Porté-Agel (2015); Niayifar and Porté-Agel (2016)). Shear, veer, and turbulence intensity measurements are typically available in field measurements and will be used to characterize atmospheric conditions in this particular model.…”

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confidence: 99%

“…The wake models available in FLORIS include the Jensen model (Jensen (1983)), the multizone wake model ), and the self-similar wake model with contributions from Porté-Agel (2014, 2016); Abkar and Porté- Agel (2015); Niayifar and Porté-Agel (2016); Dilip and Porté-Agel (2017)). Although only these three wake models are addressed and implemented in FLORIS, any wake model can be substituted into the FLORIS framework for real-time optimization of a wind farm.…”

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confidence: 99%

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Annoni¹,

Fleming²,

Scholbrock³

et al. 2018

Preprint

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Abstract.Wind turbines in a wind farm operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Wind farm controls can be used to increase power production or reduce overall structural loads by properly coordinating turbines. One wind farm control strategy that is addressed in literature is known as wake steering, wherein upstream turbines operate in yaw misaligned conditions to redirect their wakes away from downstream 5 turbines. The National Renewable Energy Laboratory (NREL) in Golden, CO conducted a demonstration of wake steering on a single utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment setpoints while a lidar mounted on the nacelle scanned five downstream distances. The lidar measurements were combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast upstream. The full-scale measurements are used to validate controls-oriented tools, including wind turbine wake models, used for wind farm controls and optimization. 10This paper presents a quantitative comparison of the lidar data and controls-oriented wake models under different atmospheric conditions and turbine operation. The results show good agreement between the lidar data and the models under these different conditions.

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“…This view, however, has been challenged. References [21,27] criticize the top hat distribution of the velocity deficit implied by the Jensen model and replace it by a Gaussian distribution to attain a better fit of the experimental data. Reference [28] reports that for two off-shore wind farms, the Jensen model underestimates production in the first few turbine rows of the wind farms, yet it overestimates production in the last few turbine rows.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Effects in Wind Farm Performance: A Regression Approach

2017

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Abstract:The optimization of turbine density in wind farms entails a trade-off between the usage of scarce, expensive land and power losses through turbine wake effects. A quantification and prediction of the wake effect, however, is challenging because of the complex aerodynamic nature of the interdependencies of turbines. In this paper, we propose a parsimonious data driven regression wake model that can be used to predict production losses of existing and potential wind farms. Motivated by simple engineering wake models, the predicting variables are wind speed, the turbine alignment angle, and distance. By utilizing data from two wind farms in Germany, we show that our models can compete with the standard Jensen model in predicting wake effect losses. A scenario analysis reveals that a distance between turbines can be reduced by up to three times the rotor size, without entailing substantial production losses. In contrast, an unfavorable configuration of turbines with respect to the main wind direction can result in production losses that are much higher than in an optimal case.

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Analytical Modeling of Wind Farms: A New Approach for Power Prediction

Cited by 267 publications

References 27 publications

Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation

Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Neighborhood Effects in Wind Farm Performance: A Regression Approach

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